∑_1^n▒{y_i (〖ln(〗⁡〖∅(β^T x_i )〗 )+(1-y_i)ln⁡〖(1-∅(β^T x_i ))〗 } 写成代码python

下面是将逻辑回归的损失函数写成Python代码的示例：

import numpy as np

def logistic_regression_loss(beta, X, y):
    """
    Logistic regression loss function
    :param beta: model parameter vector
    :param X: feature matrix
    :param y: label vector
    :return: loss value
    """
    n = X.shape[0]
    loss = 0
    for i in range(n):
        z = np.dot(X[i], beta)
        logit = 1 / (1 + np.exp(-z))
        loss += y[i] * np.log(logit) + (1 - y[i]) * np.log(1 - logit)
    loss = -loss / n
    return loss

其中，`beta`是模型参数向量，`X`是特征矩阵，每一行是一个样本的特征向量，`y`是标签向量，表示每个样本的标签。函数返回模型的损失值。